Applications of Machine Learning and Data Analytics Models in Maritime Transportation
Published on 15. February 2023
Book
Hardback
319 pages
978-1-83953-559-8 (ISBN)
Description
Machine learning and data analytics can be used to inform technical, commercial and financial decisions in the maritime industry. Applications of Machine Learning and Data Analytics Models in Maritime Transportation explores the fundamental principles of analysing maritime transportation related practical problems using data-driven models, with a particular focus on machine learning and operations research models.
Data-enabled methodologies, technologies, and applications in maritime transportation are clearly and concisely explained, and case studies of typical maritime challenges and solutions are also included. The authors begin with an introduction to maritime transportation, followed by chapters providing an overview of ship inspection by port state control, and the principles of data driven models. Further chapters cover linear regression models, Bayesian networks, support vector machines, artificial neural networks, tree-based models, association rule learning, cluster analysis, classic and emerging approaches to solving practical problems in maritime transport, incorporating shipping domain knowledge into data-driven models, explanation of black-box machine learning models in maritime transport, linear optimization, advanced linear optimization, and integer optimization. A concluding chapter provides an overview of coverage and explores future possibilities in the field.
The book will be especially useful to researchers and professionals with expertise in maritime research who wish to learn how to apply data analytics and machine learning to their fields.
Data-enabled methodologies, technologies, and applications in maritime transportation are clearly and concisely explained, and case studies of typical maritime challenges and solutions are also included. The authors begin with an introduction to maritime transportation, followed by chapters providing an overview of ship inspection by port state control, and the principles of data driven models. Further chapters cover linear regression models, Bayesian networks, support vector machines, artificial neural networks, tree-based models, association rule learning, cluster analysis, classic and emerging approaches to solving practical problems in maritime transport, incorporating shipping domain knowledge into data-driven models, explanation of black-box machine learning models in maritime transport, linear optimization, advanced linear optimization, and integer optimization. A concluding chapter provides an overview of coverage and explores future possibilities in the field.
The book will be especially useful to researchers and professionals with expertise in maritime research who wish to learn how to apply data analytics and machine learning to their fields.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 239 mm
Width: 163 mm
Thickness: 20 mm
Weight
680 gr
ISBN-13
978-1-83953-559-8 (9781839535598)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Ran Yan is a research assistant professor in the Department of Logistics and Maritime Studies at The Hong Kong Polytechnic University (PolyU), China. Dr. Yan received her Bachelor of Science degree from Hohai University in China in 2018 and her Master of Philosophy and Doctor of Philosophy degrees from The Hong Kong Polytechnic University in 2020 and 2022, respectively. Dr. Yan's research interests include applying data analytics methods and technologies to improve shipping efficiency and green shipping management. Dr. Yan has published more than 30 papers in international journals and conference proceedings, such as Transportation Research Part B/C/E, Transport Policy, Journal of Computational Science, Maritime Policy & Management, Ocean Engineering, Engineering, Sustainability, and Electronic Research Archive, and won several times of best paper/student paper award from international conferences. Dr. Yan is an editorial assistant of Cleaner Logistics and Supply Chain.
Shuaian Wang is currently Professor at The Hong Kong Polytechnic University (PolyU), China. Prior to joining PolyU, he worked as a faculty member at Old Dominion University, USA, and the University of Wollongong, Australia. Dr. Wang's research interests include big data in shipping, green shipping, shipping operations management, port planning and operations, urban transport network modeling, and logistics and supply chain management. Dr. Wang has published over 200 papers in journals such as Transportation Research Part B, Transportation Science, and Operations Research. Dr. Wang is an editor-in-chief of Cleaner Logistics and Supply Chain and Communications in Transportation Research, an associate editor of Transportation Research Part E, Flexible Services and Manufacturing Journal, Transportmetrica A, and Transportation Letters, a handle editor of Transportation Research Record, an editorial board editor of Transportation Research Part B, and an editorial board member of Maritime Transport Research. Dr. Wang dedicates to rethinking and proposing innovative solutions to improve the efficiency of maritime and urban transportation systems, to promote environmental friendly and sustainable practices, and to transform business and engineering education.
Shuaian Wang is currently Professor at The Hong Kong Polytechnic University (PolyU), China. Prior to joining PolyU, he worked as a faculty member at Old Dominion University, USA, and the University of Wollongong, Australia. Dr. Wang's research interests include big data in shipping, green shipping, shipping operations management, port planning and operations, urban transport network modeling, and logistics and supply chain management. Dr. Wang has published over 200 papers in journals such as Transportation Research Part B, Transportation Science, and Operations Research. Dr. Wang is an editor-in-chief of Cleaner Logistics and Supply Chain and Communications in Transportation Research, an associate editor of Transportation Research Part E, Flexible Services and Manufacturing Journal, Transportmetrica A, and Transportation Letters, a handle editor of Transportation Research Record, an editorial board editor of Transportation Research Part B, and an editorial board member of Maritime Transport Research. Dr. Wang dedicates to rethinking and proposing innovative solutions to improve the efficiency of maritime and urban transportation systems, to promote environmental friendly and sustainable practices, and to transform business and engineering education.
Content
Chapter 1: Introduction of maritime transportation
Chapter 2: Ship inspection by port state control
Chapter 3: Introduction to data-driven models
Chapter 4: Key elements of data-driven models
Chapter 5: Linear regression models
Chapter 6: Bayesian networks
Chapter 7: Support vector machine
Chapter 8: Artificial neural network
Chapter 9: Tree-based models
Chapter 10: Association rule learning
Chapter 11: Cluster analysis
Chapter 12: Classic and emerging approaches to solving practical problems in maritime transport
Chapter 13: Incorporating shipping domain knowledge into data-driven models
Chapter 14: Explanation of black-box ML models in maritime transport
Chapter 15: Linear optimization
Chapter 16: Advanced linear optimization
Chapter 17: Integer optimization
Chapter 18: Conclusion
Chapter 2: Ship inspection by port state control
Chapter 3: Introduction to data-driven models
Chapter 4: Key elements of data-driven models
Chapter 5: Linear regression models
Chapter 6: Bayesian networks
Chapter 7: Support vector machine
Chapter 8: Artificial neural network
Chapter 9: Tree-based models
Chapter 10: Association rule learning
Chapter 11: Cluster analysis
Chapter 12: Classic and emerging approaches to solving practical problems in maritime transport
Chapter 13: Incorporating shipping domain knowledge into data-driven models
Chapter 14: Explanation of black-box ML models in maritime transport
Chapter 15: Linear optimization
Chapter 16: Advanced linear optimization
Chapter 17: Integer optimization
Chapter 18: Conclusion